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weka.LogitBoost_REPTree

weka.LogitBoost_REPTree

Visibility: public Uploaded 05-12-2016 by Maarten Visscher Weka_3.8.0 1 runs
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  • Verified_Supervised_Classification weka weka_3.8.0
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J. Friedman, T. Hastie, R. Tibshirani (1998). Additive Logistic Regression: a Statistical View of Boosting. Stanford University.

Components

Wweka.REPTree(17)Full name of base classifier. (default: weka.classifiers.trees.DecisionStump)

Parameters

-do-not-check-capabilitiesIf set, classifier capabilities are not checked before classifier is built (use with caution).
EThe number of threads to use for batch prediction, which should be >= size of thread pool. (default 1)default: 1
HShrinkage parameter. (default 1)default: 1.0
INumber of iterations. (current value 10)default: 10
LThreshold on the improvement of the likelihood. (default -Double.MAX_VALUE)default: -1.7976931348623157E308
OThe size of the thread pool, for example, the number of cores in the CPU. (default 1)default: 1
PPercentage of weight mass to base training on. (default 100, reduce to around 90 speed up)default: 100
QUse resampling instead of reweighting for boosting.
SRandom number seed. (default 1)default: 1
WFull name of base classifier. (default: weka.classifiers.trees.DecisionStump)default: weka.classifiers.trees.REPTree
ZZ max threshold for responses. (default 3)default: 3.0
batch-sizeThe desired batch size for batch prediction (default 100).
num-decimal-placesThe number of decimal places for the output of numbers in the model (default 2).
output-debug-infoIf set, classifier is run in debug mode and may output additional info to the console

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